Image denoising via 2D dictionary learning and adaptive hard thresholding

Zhang, Xuande; Feng, Xiaoyi; Wang, Weiwei; Liu, Guojun

doi:10.1016/j.patrec.2013.07.018

Cited by 22 publications

(8 citation statements)

References 15 publications

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“…In the ideal situation, for each pixel of a clean image, the corresponding SPM has high‐linear correlation among both columns and rows of the SPM. This fact has also been experimentally verified by Zhang et al [39]. Moreover, when we represent the columns of the SPM by the same dictionary, due to the high‐linear correlation among columns of the SPM, their coefficient vectors should also be high‐linearly correlated.…”

Section: Introductionsupporting

confidence: 71%

“…For each pixel x i in image x , we extract an n × n patch

{bold-italicp}_{i, 0}

and search for a cluster of patches similar to patch

{bold-italicp}_{i, 0}

within an W × W neighbourhood window. We utilise the PCA‐based block‐matching method [39] to search for m − 1 similar patches. Specifically, the PCA [40] is performed on all the patches

falsefalse{{bold-italicp}_{j} falsefalse}_{j = 1}^{N}

extracted from the image, and the first d principle vectors are taken to form a projection matrix Φ ∈

R^{n^{2} \times d}

.…”

Section: Bilrr Denoising Algorithmmentioning

confidence: 99%

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Image denoising via bidirectional low rank representation with cluster adaptive dictionary

Wang

Feng

et al. 2016

IET Image Processing

Self Cite

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Section: Introductionsupporting

confidence: 71%

“…For each pixel x i in image x , we extract an n × n patch

{bold-italicp}_{i, 0}

and search for a cluster of patches similar to patch

{bold-italicp}_{i, 0}

within an W × W neighbourhood window. We utilise the PCA‐based block‐matching method [39] to search for m − 1 similar patches. Specifically, the PCA [40] is performed on all the patches

falsefalse{{bold-italicp}_{j} falsefalse}_{j = 1}^{N}

extracted from the image, and the first d principle vectors are taken to form a projection matrix Φ ∈

R^{n^{2} \times d}

.…”

Section: Bilrr Denoising Algorithmmentioning

confidence: 99%

Image denoising via bidirectional low rank representation with cluster adaptive dictionary

Wang

Feng

et al. 2016

IET Image Processing

Self Cite

View full text Add to dashboard Cite

“…DL method is widely used in image restoration [ 17 – 19 ], super-resolution reconstruction [ 20 – 23 ], image deblurring [ 24 – 26 ], denoising [ 27 – 32 ], medical image reconstruction [ 13 , 33 ], image prediction [ 34 ], and image inpainting [ 35 ]. However, both dynamitic atoms in each iteration step and certain noise in measurement data would increase iteration time making DL method slow in most cases.…”

Section: Ddl Algorithm In Image Analysismentioning

confidence: 99%

Recent Development of Dual-Dictionary Learning Approach in Medical Image Analysis and Reconstruction

Wang

2015

Computational and Mathematical Methods in Medicine

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As an implementation of compressive sensing (CS), dual-dictionary learning (DDL) method provides an ideal access to restore signals of two related dictionaries and sparse representation. It has been proven that this method performs well in medical image reconstruction with highly undersampled data, especially for multimodality imaging like CT-MRI hybrid reconstruction. Because of its outstanding strength, short signal acquisition time, and low radiation dose, DDL has allured a broad interest in both academic and industrial fields. Here in this review article, we summarize DDL's development history, conclude the latest advance, and also discuss its role in the future directions and potential applications in medical imaging. Meanwhile, this paper points out that DDL is still in the initial stage, and it is necessary to make further studies to improve this method, especially in dictionary training.

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“…It has successfully extended from theoretical research to a variety of applications, such as signal classification [1], image and signal denoising [2,[31][32][33][34], blind sources separation [3] and so on. So far, most denosing literatures about sparse representation are image denoising [4]- [6], and signal denoising is rarely studied. With the rapid development of science and technology, signals is interfered seriously during the transmitting.…”

Section: Introductionmentioning

confidence: 99%

An Denoising Method Based on Analysis K-SVD and Disagreement Segment and Its Application on EMI Signal

Gong

Zhao

2017

Proceedings of the 3rd International Conference on Wireless Communication and Sensor Networks (WCSN 2016)

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Abstract-Sparse representation plays an important role in signal processing. Recently, the analysis sparse representation has been attracting more and more attention. In this paper, an improved analysis K-SVD denoising algorithm based on disagreement-segment is proposed. A signal is first divided into small redundant segments, and then the signal can be denoised by the analysis K-SVD algorithm. Considering the gap between the local processing and the global signal recovery, we define a disagreement-segment as the difference between the intermediate locally denoised segment and its corresponding part in the final denoised signal. By adding the disagreementsegment to the analysis K-SVD algorithm, the denoising effect of the analysis K-SVD algorithm has a significant improvement. In addition, the experimental results show that the proposed method outperforms the analysis K-SVD algorithm and other advanced methods.

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Image denoising via 2D dictionary learning and adaptive hard thresholding

Cited by 22 publications

References 15 publications

Image denoising via bidirectional low rank representation with cluster adaptive dictionary

Image denoising via bidirectional low rank representation with cluster adaptive dictionary

Recent Development of Dual-Dictionary Learning Approach in Medical Image Analysis and Reconstruction

An Denoising Method Based on Analysis K-SVD and Disagreement Segment and Its Application on EMI Signal

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